Using Intel’s Xeon Phi for Brain Research Visualization
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Re: Using Intel’s Xeon Phi for Brain Research Visualization
#2I don't know if using Xeon Phi for rendering makes that much sense. It's sort of the problem it's least competitive to solve on a raw performance, performance per watt or development cost basis.
> However, ‘smaller’ is a relative term as current visualizations can occur on a machine that contains less than a terabyte of RAM. Traditional raster-based rendering would have greatly increased the memory consumption as the convoluted shape of each neuron would require a mesh containing approximately 100,000 triangles per neuron.
That sounds like a poor approach to this problem. You could write a shader that renders thick lines for the dendrites, and the rest of the geometry can be conventional meshes. The same shader could have a pass specially designed for lines and depth of field rendering. That's the one unusual shader. It's hard, but not super hard to write. [0]
Besides, unless you need this to run in real time (which the Xeon Phi doesn't anyway), you could just raster render and page in the mesh data from wherever. So what if it's slow.
I think highly technical platform decisions like Xeon Phi versus NVIDIA CUDA is really about the details. You have to educate the reader both on the differences that matter and why they should choose one over the other. The comment in the article, "no GPU dependencies," is a very PR-esque don't-mention-your-competitor dance around what they're actually trying to say: the CUDA ecosystem can be a pain since you can't buy the MacBook Pro with the GTX 750M easily, installing all its drivers is error-prone, SIP gets in the way of everything, Xcode and CUDA updates tend to break each other, etc. etc.
I sound like I know what I'm talking about, right? Intel's just not getting it. Show a detailed application of where Xeon Phi really excels. NVIDIA's accelerated science examples go back a decade, and some, like the accelerated grid solved Navier-Stokes fluids examples, are still state of the art.
The competition in rendering is intense. Some level of production-ready renderers like Arion, Octane and mental ray (specifically iRay, NVIDIA's GPU accelerated renderer) perform best or are exclusive to the CUDA platform. Conversely, you probably get the most flexibility from a platform like VRay or Renderman, whose support for GPU acceleration is limited. Intel embtree has a great presence today in baked lighting for game engines, but I think NVIDIA's OptiX is a lot faster.
Re: Using Intel’s Xeon Phi for Brain Research Visualization
#3"Figure 1: Even first in-silico models show the complexity and beauty of the brain"
Man the human brain is such a narcissist
Re: Using Intel’s Xeon Phi for Brain Research Visualization
#4The lowest price Xeon Phi in this generation is $2,348 (1.3ghz, 64 cores) - I can't help but feel Intel would do well to introduce an enthusiast product in to the lineup. Even 1.0ghz, 48 cores for $1000.
They're Tesla priced without an equivalent desktop gamer graphics card, and that means you can't just dip your toe into the water; you've got to buy the canoe up front.
Programming on a normal x86 doesn't really count, because there's no way to get a feel for what is fast and slow when you're using a monster of a core capable of running your poor code more quickly than it deserves.
Re: Using Intel’s Xeon Phi for Brain Research Visualization
#5I don't know if using Xeon Phi for rendering makes that much sense. It's sort of the problem it's least competitive to solve on a raw performance, performance per watt or development cost basis. > However, ‘smaller’ is a relative term as current visualizations can occur on a machine that contains less than a terabyte of RAM. Traditional raster-based rendering would have greatly increased the memory consumption as the…
> That sounds like a poor approach to this problem. You could write a shader that renders thick lines for the dendrites, and the rest of the geometry can be conventional meshes. The same shader could have a pass specially designed for lines and depth of field rendering. That's the one unusual shader. It's hard, but not super hard to write. [0]
You will be surprised how bad medical research and visualization is compared to their gaming counterparts. Most medical researchers use 5-10 year old technological approaches they learned in their PhD program.
On a side note, I have yet to see a Phi-vs-CUDA comparison. Intel is comparing Phi to Pentiums, which is utterly ridiculous.
Re: Using Intel’s Xeon Phi for Brain Research Visualization
#6The lowest price Xeon Phi in this generation is $2,348 (1.3ghz, 64 cores) - I can't help but feel Intel would do well to introduce an enthusiast product in to the lineup. Even 1.0ghz, 48 cores for $1000. They're Tesla priced without an equivalent desktop gamer graphics card, and that means you can't just dip your toe into the water; you've got to buy the canoe up front. Programming on a normal x86 doesn't really coun…
I agree with you completely. One other thing that I think Intel could/should do is to cooperate with one of the major cloud providers to offer reasonably priced by-the-hour remote access.
There is one wonderful opportunity, though, that deserves to be better known. Intel has sponsored Colfax Research to offer free online introductory courses, which include two weeks of remote access. The next session begins August 29th: http://colfaxresearch.com/how-16-08/
(I'm unaffiliated, but enjoyed the course a few months ago.)
Re: Using Intel’s Xeon Phi for Brain Research Visualization
#7The lowest price Xeon Phi in this generation is $2,348 (1.3ghz, 64 cores) - I can't help but feel Intel would do well to introduce an enthusiast product in to the lineup. Even 1.0ghz, 48 cores for $1000. They're Tesla priced without an equivalent desktop gamer graphics card, and that means you can't just dip your toe into the water; you've got to buy the canoe up front. Programming on a normal x86 doesn't really coun…
I agree - but I think there appears to be a more significant shift underpinning this. I suspect that we are beginning to see an architectural divergence between server and client.
This reverses the last 20 years where intel made inroads into the datacenter and there were few fundamental differences between xeons and their desktop brethren (the i5/i7 etc). Intel will have vastly different ISAs on server and client this coming generation (desktop is not getting AVX512). I suspect the storage layer to get bifurcated as well, since its unclear if clients will see much benefit from things like xpoint. In short client side the only tangible gains that seem to benefit off late are - will the hardware change improve battery life, will it enable thinner form factors and will it make a browser run measurably faster. I watch with great interest how Intel will push adoption of hardware features going forward on the client.
Re: Using Intel’s Xeon Phi for Brain Research Visualization
#8One of the interesting things to keep in mind is that these new Xeon Phi cards can be used as standalone CPUs, not just as PCIe cards like a GPU. This is the "self-hosted mode" the article talks about. So one can now think about comparing a lone Xeon Phi doing both jobs versus a CPU plus an NVidia GPU.
Re: Using Intel’s Xeon Phi for Brain Research Visualization
#9The lowest price Xeon Phi in this generation is $2,348 (1.3ghz, 64 cores) - I can't help but feel Intel would do well to introduce an enthusiast product in to the lineup. Even 1.0ghz, 48 cores for $1000. They're Tesla priced without an equivalent desktop gamer graphics card, and that means you can't just dip your toe into the water; you've got to buy the canoe up front. Programming on a normal x86 doesn't really coun…
As someone who has programmed both Phis and conventional x86 CPUs, I can confirm that the Phi is more sensitive to data traversal order and NUMA effects on which core accesses which memory. Also, the latest generation (Knights Landing) has much better performing cores than the previous generation.
Re: Using Intel’s Xeon Phi for Brain Research Visualization
#10The lowest price Xeon Phi in this generation is $2,348 (1.3ghz, 64 cores) - I can't help but feel Intel would do well to introduce an enthusiast product in to the lineup. Even 1.0ghz, 48 cores for $1000. They're Tesla priced without an equivalent desktop gamer graphics card, and that means you can't just dip your toe into the water; you've got to buy the canoe up front. Programming on a normal x86 doesn't really coun…
As someone who has programmed both Phis and conventional x86 CPUs, I can confirm that the Phi is more sensitive to data traversal order and NUMA effects on which core accesses which memory. Also, the latest generation (Knights Landing) has much better performing cores than the previous generation.
Well, if it weren't the case and you had that core count without compromise, Phis would have come along a lot sooner with a price tag to match :)
Did you happen to use the Knight's Corner or the new Knight's Landing variant? I'd be quite interested to know how KL stacks up, as naively from the specs it seems like it should be a lot more tolerant with code (but not poor memory access patterns).